Multivariate statistical analysis of bioavailability of heavy metals and mineral characterization in selected species of coastal flora

This study presents a thorough investigation into the concentration of heavy metals and mineral composition within four distinct coastal flora species: Cyperus conglomeratus, Halopyrum mucronatum, Sericostem pauciflorum, and Salvadora persica. Employing rigorous statistical methodologies such as Pearson coefficient correlation, principal component analysis (PCA), analysis of variance (ANOVA), and interclass correlation (ICC), we aimed to elucidate the bioavailability of heavy metals, minerals, and relevant physical characteristics. The analysis focused on essential elements including copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), magnesium (Mg2+), calcium (Ca2+), sodium (Na+), potassium (K+), and chloride (Cl–), all of which are known to play pivotal roles in the ecological dynamics of coastal ecosystems. Through PCA, we discerned distinctive patterns within PC1 to PC4, collectively explaining an impressive 99.65% of the variance observed in heavy metal composition across the studied flora species. These results underscore the profound influence of environmental factors on the mineral composition of coastal flora, offering critical insights into the ecological processes shaping these vital ecosystems. Furthermore, significant correlations among mineral contents in H. mucronatum; K+ with content of Na+ (r = 0.989) and Mg2+ (r = 0.984); as revealed by ICC analyses, contributed to a nuanced understanding of variations in electrical conductivity (EC), pH levels, and ash content among the diverse coastal flora species. By shedding light on heavy metal and mineral dynamics in coastal flora, this study not only advances our scientific understanding but also provides a foundation for the development of targeted environmental monitoring and management strategies aimed at promoting the ecological sustainability and resilience of coastal ecosystems in the face of ongoing environmental challenges.

The word halophyte has been coined and introduced in the language of botany since the mid-nineteenth century, as it has been well established in the last decade 1 .Halophytes are salt-resistant or salt-tolerant plants and have a remarkable ability to complete their life cycle in saline conditions.During evolution, they have developed different morphological, anatomical and physiological strategies to proliferate in high-salt environments [2][3][4][5][6][7][8][9][10][11][12] .Some investigations reflect that some salt-tolerant species require salt content for their growth [13][14][15] .Na + /H + exchange was significantly higher in vesicles made from leaves and increased with plant salinity 16 .Halophytes require appropriate standardization of Na + and K + concentrations inside their cells, referring to a resistance mechanism to hydroxyl radicals 17 .The response of plants to natural environmental behavior differs from that of saline strains with combined stress alone 6 .The regulation of salt tolerance capacity is mainly based on the connection or association between salt and water.Halophytes are able to reduce the accumulation of salts in the solution in the plant tissue due to which the amount of salt liberated to the leaves is absorbed by the salivary glands through growth and ion secretion 18 .Furthermore, the study of halophytic species has broader implications beyond their ecological

Experimental work
Leaves of two monocotyledonous plant species Cyprus conglomeratus and Halopyrum mucronatum and two dicotyledonous plant species Sericostoma pauciflorum and Salvadora persica were collected during monsoon, winter and summer seasons.The material was washed and dried in room temperature or in oven (60 to 80 °C). 1 g of dried plant material was taken in silica crucible and ashed in burner to complete oxidation of the organic matter and add 1 ml of concentrated nitric acid.The acid was evaporated in water bath and crucible were again placed in burner for complete ashing.After cooling the crucible add 20 ml 1:1 hydrochloric acid and the extract was evaporated in water bath.Final addition of 20 ml distilled water and then extract filtered through Whatman filter paper no.44 with repeated washing and final volume made up 250 ml with distilled volume.This sample were used for the estimation of calcium (Ca 2+ ) and magnesium (Mg 2+ ) by EDTA (ethylene diamine tetra acetic acid) titration, sodium (Na + ) and potassium (K + ) by flame photometry (Systronics-130 India) method 35 .
1 g of dried plant material was boiled in 100 ml of deionized water in a water bath for 1 h and the extract was filtered.A filtered sample was used for the estimation of chloride (Cl -) by the argentometric method 36 , pH through pH meter (Eutech India) and Electrical conductivity for EC meter (Systronics-306 India).

Multivariate statistical analysis
The analysis of bioavailability of heavy metal and mineral characterization in coastal flora, Pearson coefficient correlation, principal component analysis (PCA), analysis of variance (ANOVA) and interclass correlation (ICC) were performed using commercial software (SPSS).The coefficient correlation measured the interactive strength of two variables.PCA and ICC are the incredibly usual multivariate statistical methods used in environmental research 37 .

Principle component analysis (PCA)
The Principal Component Analysis (PCA) on the loading of heavy metals on Halopyrum mucronatum, highlighting the significant findings (Table 1).The PCA aimed to identify the main variables contributing to the variance in heavy metal concentrations.Four principal components (PCs) were extracted and analyzed for their respective loadings of each metal.The first principal component (PC1) emerges as the most significant, with an eigenvalue of 4.88899.This component explains a substantial proportion of the total variance in the dataset.Notably, Cu (− 0.00993) and Fe (0.99995) exhibit the highest loadings on PC1, indicating a strong correlation between these metals.Their association suggests that variations in Cu and Fe concentrations in Halopyrum mucronatum are www.nature.com/scientificreports/closely linked.PC2, while contributing a relatively smaller amount to the overall variance, still provides valuable insights.Mn (0.61695) and Zn (0.76142) display the highest loadings on PC2, indicating a positive correlation between these metals.The relationship between Mn and Zn concentrations becomes noteworthy, although their influence on the overall variance is less pronounced compared to PC1.Regarding PC3, with an eigenvalue of 0.00233158, it contributes to the variance to a lesser extent.Nonetheless, Mn (0.78584) shows the highest loading on PC3, emphasizing its significance in explaining the variance along this component.Finally, PC4 demonstrates the smallest eigenvalue of 0.00017976, suggesting the least contribution to the overall variance.However, Cu (0.97449) exhibits the highest loading on PC4, indicating a strong influence of Cu on this component.Overall, the PCA results highlight the significant role of Cu, Fe, Mn, and Zn as the primary heavy metals contributing to the observed variance in Halopyrum mucronatum.Cu and Fe exhibit the most substantial impact, as evidenced by their contributions to the highest eigenvalue and loadings on PC1.
The significance of the study lies in its ability to elucidate the intricate relationships between heavy metal concentrations in Halopyrum mucronatum, a plant species often used in phytoremediation efforts due to its metal tolerance.By employing Principal Component Analysis (PCA), the study identifies the key variables contributing to the variance in heavy metal concentrations within the plant.The findings underscore the prominent roles of copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) in influencing the observed variance.Specifically, the first principal component (PC1) emerges as the most significant, with Cu and Fe exhibiting the highest loadings.This indicates a strong correlation between Cu and Fe concentrations in Halopyrum mucronatum, suggesting that variations in these metals are closely linked within the plant.Moreover, while PC2, PC3, and PC4 contribute to the variance to a lesser extent, they still provide valuable insights into the relationships between other heavy metals such as Mn and Zn.These findings offer a comprehensive understanding of the interplay between different heavy metals within the plant species.The significance of this study extends beyond the identification of heavy metal concentrations; it provides crucial insights for environmental scientists, agronomists, and policymakers involved in phytoremediation and environmental management.Understanding the dynamics of heavy metal accumulation and correlation within specific plant species like Halopyrum mucronatum is vital for designing effective remediation strategies and mitigating the impacts of metal pollution on ecosystems and human health.Furthermore, the study's methodology showcases the applicability of PCA in unraveling complex datasets, thereby contributing to the broader field of environmental science and analytical chemistry.
In Cyprus conglomeratus, the principal component analysis (PCA) conducted on the loading of heavy metals and highlighting the significant findings (Table 2).The objective of the PCA was to identify the main variables contributing to the variance in heavy metal concentrations.The table presents the loadings for each metal across four principal components (PCs).The first principal component (PC1) emerges as the most significant, as it possesses the highest eigenvalue of 1.03602.PC1 explains a substantial proportion of the total variance in the dataset.Cu (0.0099916) exhibits the highest loading on PC1, indicating a positive correlation between Cu concentrations and the variance observed in Cyprus conglomeratus.This suggests that changes in Cu levels are likely to have a significant impact on the overall variability of heavy metal concentrations in this plant species.PC2, with an eigenvalue of 0.0181766, contributes to a notable portion of the overall variance.Fe (0.99484) demonstrates the highest loading on PC2, implying a strong positive correlation between Fe concentrations and the variance observed along this component.This finding suggests that variations in Fe levels may contribute significantly to the observed differences in heavy metal concentrations in Cyprus conglomeratus.PC3, with an eigenvalue of 0.00278129, contributes to the variance to a lesser extent.Mn (0.52262) displays the highest loading Overall, the PCA results highlight the significant role of Cu, Fe, Mn, and Zn as the primary heavy metals contributing to the observed variance in Cyprus conglomeratus.Cu and Fe exhibit the most substantial impact, as evidenced by their contributions to the highest eigenvalue and loadings on PC 1 and PC 2, respectively.
The significance of the study on heavy metal concentrations in Cyprus conglomeratus, as determined through Principal Component Analysis (PCA), is multifaceted and holds implications for both environmental science and ecological management.Firstly, by identifying the main variables contributing to the variance in heavy metal concentrations within Cyprus conglomeratus, this study offers critical insights into the dynamics of heavy metal accumulation in this plant species.Understanding which metals have the most substantial impact on the observed variability allows for targeted monitoring and remediation efforts in environments where Cyprus conglomeratus grows.The findings highlight the significant roles of copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) in influencing the observed variance.Notably, the strong positive correlation between Cu concentrations and the variance observed in PC1 suggests that changes in Cu levels may serve as indicators for overall heavy metal variability in Cyprus conglomeratus.Similarly, the high loading of Fe on PC2 underscores the importance of iron concentrations in explaining differences in heavy metal levels within the plant species.This analytical approach can be valuable for researchers and environmental practitioners involved in assessing metal pollution and devising effective mitigation strategies.In a broader context, understanding heavy metal accumulation in Cyprus conglomeratus contributes to our knowledge of plant-metal interactions and phytoremediation potential.This knowledge can inform decisions regarding the use of Cyprus conglomeratus in environmental restoration projects aimed at mitigating metal contamination in soils and water bodies.Overall, the study provides valuable insights into the factors influencing heavy metal concentrations in Cyprus conglomeratus, thereby contributing to the advancement of environmental science and the development of sustainable strategies for managing metal pollution in natural ecosystems.
The PCA conducted on the loading of heavy metals on Sericostoma pauciflorum, shedding light on the significant findings (Table 3).The PCA aimed to identify the main variables contributing to the variance in heavy metal concentrations.The table displays the loadings for each metal across four principal components (PCs).The first principal component (PC1) stands out as the most significant, featuring the highest eigenvalue of 1.31452.PC1 explains a substantial proportion of the total variance in the dataset.Cu (0.008826) exhibits the highest loading on PC1, indicating a positive correlation between Cu concentrations and the observed variance in Sericostoma pauciflorum.This suggests that variations in Cu levels are likely to have a significant impact on the overall variability of heavy metal concentrations in this species.PC2, with an eigenvalue of 0.0118029, contributes to a relatively smaller portion of the overall variance.Mn (0.98898) demonstrates the highest loading on PC2, implying a strong positive correlation between Mn concentrations and the variance observed along this component.This finding suggests that variations in Mn levels may contribute significantly to the observed differences in heavy metal concentrations in Sericostoma pauciflorum.PC3, with an eigenvalue of 0.000276787, contributes to the variance to a lesser extent.Cu (0.78811) displays the highest loading on PC3, indicating its significance in explaining the variance along this component.The positive loading suggests that changes in Cu concentrations are associated with the observed variability in heavy metal levels in Sericostoma pauciflorum.Similarly, PC4 exhibits a relatively smaller eigenvalue of 7.49223E-05, indicating its minimal contribution to the overall variance.Zn (0.79132) demonstrates the highest loading on PC4, suggesting a positive correlation between Zn concentrations and the observed variance.The loading indicates that changes in Zn levels may have a lesser but still notable impact on the overall variability of heavy metal concentrations in Sericostoma pauciflorum.
The principal component analysis (PCA) conducted on the loading of heavy metals in Sericostoma pauciflorum provides critical scientific insights into the intricate dynamics of metal accumulation within this aquatic species.By identifying copper (Cu) and manganese (Mn) as the primary variables contributing to the observed variance, the study elucidates the major drivers of heavy metal concentrations in Sericostoma pauciflorum populations.The strong positive correlation between Cu concentrations and the variance observed in PC1 underscores the significant role of copper in influencing overall heavy metal variability, while the substantial loading of Mn on PC2 highlights the importance of manganese concentrations in explaining differences in metal levels within the species.These findings deepen our understanding of metal dynamics in marine water habitats where Sericostoma Table 3. PCA loading of heavy metals on Sericostoma pauciflorum.Moreover, the application of PCA in this study showcases its utility as an analytical tool for unraveling complex datasets in environmental science, providing a methodological framework for future research endeavors aimed at understanding and mitigating metal pollution in aquatic ecosystems.Ultimately, these findings contribute to the broader scientific knowledge base concerning environmental pollution and offer insights that can inform management and conservation efforts aimed at preserving the health and biodiversity of marine water ecosystems.

Variables
In Salvadora persica, the PCA conducted on the loading of heavy metals and providing valuable insights into the significant findings (Table 4).The PCA aimed to identify the main variables contributing to the variance in heavy metal concentrations.The table displays the loadings for each metal across four principal components (PCs).The first principal component (PC1) emerges as the most significant, explaining a substantial proportion of the total variance with a percentage of the variance of 77.592%.Cu (− 0.1615) exhibits the highest loading on PC 1, indicating a negative correlation between Cu concentrations and the observed variance in Salvadora persica.On the other hand, Fe (0.98456) shows a strong positive loading on PC1, suggesting a positive correlation between Fe concentrations and the variance.These findings suggest that Cu and Fe concentrations contribute significantly to the observed variability in heavy metal levels in Salvadora persica.PC2, with a percentage of variance of 21.985%, contributes a notable portion to the overall variance.Cu (0.98487) exhibits the highest loading on PC2, indicating its significance in explaining the variance along this component.The positive loading suggests a positive correlation between Cu concentrations and the variance observed along PC2.In PC3, with a percentage of variance of 0.42223%, contributes to the variance to a lesser extent.Mn (0.59021) displays the highest loading on PC 3, indicating its importance in explaining the variance along this component.The positive loading suggests a positive correlation between Mn concentrations and the observed variability in heavy metal levels in Salvadora persica.
The Principal Component Analysis (PCA) study on heavy metal loadings in Salvadora persica holds significant implications for marine environment soil dynamics.While Salvadora persica primarily thrives in terrestrial habitats, its proximity to coastal areas and ability to tolerate saline soils make it a valuable indicator species for understanding metal accumulation in environments adjacent to marine ecosystems.By identifying copper (Cu), iron (Fe), manganese (Mn), and other heavy metals as key variables contributing to the observed variance, the study offers insights into the potential pathways through which metals can enter marine ecosystems.Moreover, understanding how Salvadora persica accumulates heavy metals provides valuable information for assessing the risk of metal transfer to marine organisms through ecological pathways, including bioaccumulation and biomagnification.The findings also highlight the potential for Salvadora persica to serve as a tool for phytoremediation efforts in coastal soils, thus reducing the risk of metal leaching into marine environments.By incorporating data from terrestrial plants like Salvadora persica into integrated coastal zone management plans, policymakers can develop more effective strategies for preserving coastal biodiversity and mitigating the impacts of pollution on marine environments.Ultimately, this study contributes to our broader understanding of the interconnectedness between terrestrial and marine ecosystems and informs efforts to safeguard the health and integrity of coastal environments.

Pearson coefficient correlation
The results (Table 5) of the Pearson correlation in the ionic composition of H. mucronatum leaf indicated that the negative and non-significant (P > 0.05) correlation observed in Cl -with Ca 2+ (r = − 0.645), Mg 2+ (r = − 0.684), Na + (r = − 0.751) and K + (− 0.720).The concentration of Ca 2+ was positive and significantly (P < 0.05) correlated with Na + (r = 0.800), K + (r = 0.856) and Mg 2+ (r = 0.840); however, the concentration of Mg 2+ H. mucronatum leaf was positive and highly significant (P < 0.01) with Na + (r = 0.971) and K + (r = 0.984).Although, a positive and highly significant correlation was observed between the amount of Na + with the K + (r = 0.989) in this plant leaf sample.The described results denoted that the H. mucronatum leaf ionic composition was internally positive and significantly correlated with each other in except for the Cl -content.The concentration of Cl -in this plant leaf was not significantly correlated with the Ca 2+ , Mg 2+ , Na + and K + ; it was noted that the concentration of the Cl -was negative and non-significant with other elements.
In the C. conglomeratus, the positive and significant (P < 0.01) correlation observed between Na + with K + (r = 0.708); However, the positive but non-significant (P > 0.05) correlation was observed with Cl -(r = 0.465) and Mg 2+ (r = 0.072) and negative non-significant correlated with Ca 2+ (r = − 0.483).The Cl -content of C. conglomeratus leaves was negatively non-significant with Ca 2+ (r = − 0.348) when the positively non-significant with Na + (r = 0.465), K + (r = 0.123) and Mg 2+ (r = 0.016).The Pearson correlation analysis of C. conglomeratus leaf parameters showed that the leaf Ca 2+ showed negative and non-significant (P > 0.05) relationship with other www.nature.com/scientificreports/ions viz., Na + (r = − 0.483), K + (r = − 0.226), Mg 2+ (r = − 0.044) and Ca 2+ as described earlier.Also, a positive and non-significant (P > 0.05) correlation was observed in the concentration of Mg 2+ with Na + (r = 0.072) and K + (r = 0.413) of this plant leaf.These results mentioned the mean values of C. conglomeratus leaf samples during the winter, monsoon and summer seasons.Based on the observations of the Pearson correlation of this plant leaf, it described that only Na + significantly correlated with K + ; rest of the elements not significant correlation observed in this plant species.The Pearson correlation of ionic composition of the S. pausiflorum leaf indicated that there is no positive or negative significant (P < 0.05) correlation observed between any inorganic ions based on their preliminary data collected during the three different seasons.The K + was positive and non-significantly (P > 0.05) correlated with Ca 2+ (r = 0.579), Mg 2+ (r = 0.225), Na + (r = 0.065) and Cl -(r = 0.658).The negative and non-significant correlation observed between the Ca 2+ with Cl -(r = − 0.078), Na + (r = − 0.499) and Mg 2+ (r = − 0.012); also, the Mg 2+ negative and non-significantly correlated with Na + (r = − 0.325).The Pearson correlation of the S. persica plant ionic constitute and indicated a negative and significant correlation (P < 0.05) in Na + with K + (r = − 0.716).The Na + was negative and non-significantly (P > 0.05) correlated with Ca 2+ (r = 0.615); Except Na + with K + and Ca 2+ all other ionic constitutes were positive and non-significant.

Major findings
The correlations between different ions in the leaves varied across plant species, indicating species-specific differences in ion regulation and interaction.While some elements showed significant correlations, others did not, suggesting complex regulatory mechanisms governing ion composition in plant leaves.

Internal ion regulation
Across all plant species studied, there is evidence of internal ion regulation, with certain ions showing consistent positive correlations, such as Ca 2+ with Na + , K + , and Mg 2+ in H. mucronatum, and Na + with K + in C. conglomeratus.

Species-specific differences
Each plant species demonstrates unique patterns of ion interactions.For instance, while Cl -does not show significant correlations with other ions in H. mucronatum and C. conglomeratus, it exhibits a positive but nonsignificant correlation with K + in S. pausiflorum.

Complex regulatory mechanisms
The presence of both significant and non-significant correlations suggests complex regulatory mechanisms governing ion composition in plant leaves.These mechanisms likely involve intricate interactions between various ions and physiological processes within each plant species.

ANOVA and interclass correlation (ICC)
The results of an analysis of variance (ANOVA) and interclass correlation (ICC) analysis conducted on Ash, pH, and EC (electrical conductivity) parameters in the selected location of Halopyrum mucronate (Table 6).The ANOVA results indicate significant variations among raters for Ash (F = 152.6,p < 0.05), whereas no significant variations were observed between cases (F = 0.9471, p > 0.05).The within-case analysis revealed a substantial variation for all parameters, with mean squares ranging from  (2, 1)) value represents the ICC calculated for each specific variable within the same location.The reported values are − 0.0007787 for EC, − 0.0007787 for pH, and − 0.0007787 for ash content.Mean ICC (ICC (2, k)): This value represents the average ICC calculated across all locations for each variable.The reported mean ICC values are − 0.00234 for EC, − 0.00234 for pH, and − 0.00234 for ash content.In Model 3, individual ICC (ICC (3, 1)): This value represents the ICC calculated for each specific variable within the same location.The reported values are − 0.01795 for EC, − 0.01795 for pH, and − 0.01795 for ash content.Mean ICC (ICC (3, k)): This value represents the average ICC calculated across all locations for each variable.The reported mean ICC values are − 0.05585 for EC, − 0.05585 for pH, and − 0.05585 for ash content.
In Cyprus conglomeratus, the results of an analysis of the EC (electrical conductivity), pH (potential of hydrogen), and ash content were reported from various locations (Table 7).The statistical analysis includes calculations such as the sum of squares, degrees of freedom, mean square, and F-value, which are used to assess the variability and significance of the measured parameters.In terms of the variability between raters, the analysis indicates a sum of squares of 905.949 with 2 degrees of freedom, resulting in a mean square of 452.974.The calculated F-value of 218 suggests a significant difference between the raters in terms of their evaluations of the collected samples.Regarding the variability between cases, the sum of squares is reported as 22.621, with 10 degrees of freedom, leading to a mean square of 2.2621.The associated F-value of 1.089 indicates a relatively small difference between the cases in terms of the measured parameters.The within-case variability is represented by a sum of squares of 947.498 with 22 degrees of freedom, resulting in a mean square of 43.0681.This within-case variability reflects the natural variation of the measured parameters within each individual sample.The residual variability, which represents the unexplained variability after accounting for the other sources of variation, is reported as 41.5494 with 20 degrees of freedom, resulting in a mean square of 2.07747.This residual variability could be due to measurement error or other unaccounted factors.The total variability, obtained by summing the between-case, within-case, and residual variabilities, is reported as 970.119 with 32 degrees of freedom.
To assess the agreement or consistency between the raters and the overall mean, the intraclass correlation coefficients (ICCs) are calculated.In Model 1, the ICC (1, 1) for individual raters is − 0.4616, indicating poor agreement among the raters for evaluating the collected samples.Similarly, the ICC (1, k) for the mean of all www.nature.com/scientificreports/raters is − 18.04, suggesting substantial inconsistency in the overall mean measurements.In Model 2, the ICC (2, 1) for individual raters is 0.001427, indicating minimal agreement among the raters.The ICC (2, k) for the mean of all raters is 0.004269, further supporting the lack of agreement in the overall mean measurements.In Model 3, the ICC(3, 1) for individual raters is 0.02877, suggesting a slightly higher level of agreement among the raters compared to the previous models.The ICC (3, k) for the mean of all raters is 0.08162, indicating a slight improvement in the agreement for the overall mean measurements.
The results of an analysis of the EC (electrical conductivity), pH (potential of hydrogen), and ash content of Sericostoma pauciflorum samples collected from different locations (Table 8).Similar to the previous example, the statistical analysis includes calculations such as the sum of squares, degrees of freedom, mean square, and F-value to assess the variability and significance of the measured parameters.In terms of the variability between raters, the analysis indicates a sum of squares of 1931.01 with 2 degrees of freedom, resulting in a mean square of 965.504.The calculated F-value of 38.61 suggests a significant difference between the raters in terms of their evaluations of the collected samples.Regarding the variability between cases, the sum of squares is reported as 124.191, with 5 degrees of freedom, leading to a mean square of 24.8381.The associated F-value of 0.9934 indicates a relatively small difference between the cases in terms of the measured parameters.The within-case variability is represented by a sum of squares of 2181.05 with 12 degrees of freedom, resulting in a mean square of 181.754.This within-case variability reflects the natural variation of the measured parameters within each individual sample.The residual variability, which represents the unexplained variability after accounting for the other sources of variation, is reported as 250.044 with 10 degrees of freedom, resulting in a mean square of 25.0044.This residual variability could be due to measurement error or other unaccounted factors.The total variability, obtained by summing the between-case, within-case, and residual variabilities, is reported as 2305.24 with 17 degrees of freedom.
To assess the agreement or consistency between the raters and the overall mean, the Intraclass Correlation Coefficients (ICCs) are calculated.In Model 1, the ICC (1, 1) for individual raters is − 0.4041, indicating poor agreement among the raters for evaluating the collected samples.Similarly, the ICC (1, k) for the mean of all raters is -6.318, suggesting substantial inconsistency in the overall mean measurements.In Model 2, the ICC (2, 1) for individual raters is -0.000305, indicating minimal agreement among the raters.The ICC (2, k) for the mean of all raters is -0.0009156, further supporting the lack of agreement in the overall mean measurements.In Model 3, the ICC (3, 1) for individual raters is − 0.002221, suggesting a slightly higher level of agreement among  www.nature.com/scientificreports/ the raters compared to the previous models.The ICC (3, k) for the mean of all raters is -0.006694, indicating a slight improvement in the agreement for the overall mean measurements.The results (Table 9) provide important information about the pH, EC (electrical conductivity), and ash content of Salvadora persica samples collected from different locations.It helps us understand how these properties vary among the samples and how reliable the measurements.Regarding the variability between raters, a significant difference was observed (F = 36.3,p < 0.05), as indicated by the sum of squares of 787.985 and 2 degrees of freedom.This suggests that the raters' evaluations of the collected samples exhibited considerable variation for the measured parameters.In terms of the variability between cases, there was a relatively small difference (F = 1.045, p > 0.05), as reflected by the sum of squares of 34.0223 and 3 degrees of freedom.This indicates that the samples collected from different locations had comparable measurements for the investigated parameters.The within-case variability, which represents the inherent variation within each individual sample, was assessed through the sum of squares of 853.112 with 8 degrees of freedom.The total variability, obtained by summing the between-rater, between-case, and within-case variabilities, was 887.134 with 11 degrees of freedom.
Model 1 examines the agreement or consistency among the raters in evaluating the pH, EC, and ash content of the Salvadora persica samples.The Individual ICC (1, 1) value of − 0.4243 indicates poor agreement among the raters, implying that their evaluations of the samples show significant variability.The 95% confidence interval [− 0.4854, 0.154] further confirms the uncertainty in their agreement.Similarly, the Mean ICC(1, k) value of − 8.403, along with the 95% confidence interval [− 49.93, 0.3533], indicates a substantial inconsistency in the overall mean measurements.This suggests that the raters' assessments of the samples differ significantly, leading to inconsistency in the average values derived from their evaluations.These results emphasize the need for improved agreement and reliability among raters when assessing the pH, EC, and ash content of Salvadora persica samples.
In Model 2, the focus is on assessing the agreement or consistency among raters and the overall mean measurements for the pH, EC, and ash content of the Salvadora persica samples.The Individual ICC (2, 1) value of 0.001518 suggests minimal agreement among the raters, indicating that their evaluations show only slight consistency.The 95% confidence interval [− 0.02905, 0.3272] further underscores the uncertainty in their agreement.Similarly, the Mean ICC (2, k) value of 0.00454, along with the 95% confidence interval [− 0.09254, 0.5934], indicates limited agreement in the overall mean measurements.This implies that the raters' assessments of the samples and the average values derived from their evaluations exhibit only slight consistency.Therefore, there is room for improvement in achieving a higher level of agreement among the raters and in obtaining more consistent mean measurements for these parameters.
Model 3 aims to determine the agreement or consistency among raters and the overall mean measurements for the pH, EC, and ash content of the Salvadora persica samples.The Individual ICC (3, 1) value of 0.01472 suggests a slightly higher level of agreement among the raters compared to the previous models.However, the 95% confidence interval [− 0.39, 0.8275] indicates some uncertainty in their agreement.The Mean ICC (3, k) value of 0.04289, along with the 95% confidence interval [− 5.316, 0.935], suggests a slight improvement in the agreement for the overall mean measurements.While there is some consistency observed among the raters and in the mean measurements, there are still variations and uncertainties present.These findings emphasize the importance of further enhancing the agreement among raters and striving for more consistent mean measurements for accurate assessment of the pH, EC, and ash content of Salvadora persica samples.

Major findings
The significance of the study lies in its contribution to several key areas within plant science and research methodology: Understanding plant physiology By analyzing the variability and correlations in the ionic composition and other parameters of different plant species, the study provides insights into the underlying physiological processes governing plant growth, development,

Quality assurance in data collection
The study highlights the importance of standardized measurement protocols and rigorous quality control measures in ensuring the reliability and consistency of data collected in plant science research.By identifying sources of variability and potential measurement errors, the study underscores the need for systematic approaches to data collection and analysis.

Enhancing RESEARCH methodology
Through the application of statistical analyses such as ANOVA and ICC, the study demonstrates the use of advanced research methodologies to assess variability, correlations, and agreement in plant parameters measured by multiple raters or across different locations.These methodologies can serve as valuable tools for researchers in designing experiments, analyzing data, and drawing meaningful conclusions from their findings.

Informing agricultural practices
The findings of the study have practical implications for agricultural management, as they provide insights into the factors influencing the nutrient content, pH levels, and other important parameters in plant species.This information can guide farmers and agricultural practitioners in making informed decisions related to crop selection, soil fertility management, and nutrient supplementation.

Supporting environmental conservation
By studying the variability and correlations in plant parameters across different locations, the study contributes to our understanding of ecosystem dynamics and biodiversity conservation.This knowledge can inform conservation strategies aimed at protecting plant species and their habitats in the face of environmental challenges such as climate change and habitat loss.

Facilitating future prospectives
The study lays the groundwork for future research endeavors by identifying areas for further investigation and refinement.By highlighting the limitations and challenges associated with current methodologies, the study paves the way for future studies aimed at addressing these issues and advancing our understanding of plant biology and ecology.In summary, the significance of the study lies in its contribution to advancing knowledge in plant science, improving research methodology, informing agricultural practices, supporting environmental conservation efforts, and laying the groundwork for future research endeavors in these important areas.

Discussion
The study presents a comprehensive analysis of heavy metal bioavailability and mineral characterization in halophytic plants using multivariate statistical techniques.Here, we discuss the findings of the study along with relevant literature to contextualize the results and implications.The study likely employed multivariate statistical methods such as principal component analysis (PCA) to identify patterns of heavy metal bioavailability in halophytic plants.PCA might have been used to reduce the dimensionality of the dataset, identifying dominant factors influencing heavy metal uptake and accumulation in different plant species.Calone et al. 38 performed the PCA in six halophytic species in control and control normalized PCA to 74.4% and 72.2% of total variance, respectively; however, 99.65% of total variance were observed through PCA in selected flora in this study.Wuana and Okieimen 39 and Zeng et al. 40 discuss the complex mechanisms governing heavy metal uptake in plants, emphasizing the importance of understanding soil-plant interactions and physiological processes.Song et.al. 41 was statistically analyzed the element concentration in the leaf of halophytic species with the soil elements concentration and recorded a significant (P < 0.001) positive correlation between Ca 2+ with Mg 2+ and K + in the soil elements; however, leaf K + was positive and significantly (P < 0.001) correlated with leaf Ca 2+ while the leaf Na + was negative and significantly (P < 0.001) correlated with K + and Ca 2+ .It was also recorded that there was a non-significantly (P > 0.05) correlated soil mineral with leaf ionic components but soil minerals were also significantly (P < 0.05; P < 0.01 and P < 0.001) correlated with leaf heavy metals.Also, it was analyzed ANOVA in mineral concentration with salt-tolerance type and plant parts (leaf, stem and root) and reported significant effects on the concentration of Na + (F = 14.88;P < 0.001), K + (F = 4.90; P < 0.05), Ca 2+ (F = 9.68; P < 0.01) and Mg 2+ (F = 10.14;P < 0.01) in the salt-tolerance type; however, the plant parts were significantly affected by Na + (F = 14.07;P < 0.001), Ca 2+ (F = 3.09; P < 0.05) and Mg 2+ (F = 21.71;P < 0.001) concentration and non-significant affected by K + (F = 0.82; P > 0.05) content.In the dicot plants, a very highly significant difference was observed in ash content as well as Na + and Cl -content and significant observed in Ca 2+ at the species level and non-significant relation was observed at the seasonal level; however, in the monocot species, the significant difference observed only Ca 2+ in the seasonal level 42 .The research paper provides valuable insights into the bioavailability of heavy metals and mineral characterization in halophytic plants using multivariate statistical analysis.By elucidating the mechanisms governing metal uptake and plant adaptation in saline environments, the study contributes to our understanding of halophyte ecology and their potential applications in phytoremediation and environmental management.

Conclusion
In conclusion, this research paper examined the variability and agreement among raters in evaluating different plant species, namely Halopyrum mucronatum, Cyprus conglomeratus, Sericostoma pauciflorum, and Salvadora persica.The findings from the principal component analysis (PCA) highlighted the significant contributions of certain heavy metals (Cu, Fe, Mn) to the observed variance in each species, emphasizing their influential roles in determining the variability in heavy metal concentrations.The selected plant shows a significant correlation between some mineral constituents like Halopyrum mucronatum all macronutrients show a positive significant correlation except Cl -; while Sericostoma pauciflorum does not show any significance of the two variables.Furthermore, the ANOVA and intraclass correlation coefficients (ICC) analyses revealed valuable insights into the consistency and reliability of measurements within and between raters.While there may be room for improvement in achieving higher levels of agreement among raters, the results indicate the potential for enhancing measurement consistency in future evaluations.The differences between cases were relatively small, indicating that the measured parameters showed comparable values across different locations.These findings highlight the importance of ongoing efforts to address inter-rater variability and promote greater agreement among raters, ultimately contributing to more reliable assessments of the samples.Overall, this study underscores the importance of addressing inter-rater variability and enhancing measurement consistency when evaluating plant species.It highlights the influential heavy metals and their correlations, providing valuable information for future research and management of these plant species.These findings contribute to the understanding of the variability and reliability of measurements in the context of ecological and environmental studies, and emphasize the need for standardized protocols and improved agreement among raters to ensure accurate and consistent assessments. https://doi.org/10.1038/s41598-024-62201-0www.nature.com/scientificreports/

Table 1 .
PCA loading of heavy metals on Halopyrum mucronatum.Significant values are in bold.

Table 2 .
PCA loading of heavy metals on Cyprus conglomeratus.on PC3, indicating its significance in explaining the variance along this component.The positive loading suggests that changes in Mn concentrations are associated with the observed variability in heavy metal levels in Cyprus conglomeratus.Similarly, PC4 exhibits a relatively smaller eigenvalue of 0.000234194, indicating its minimal contribution to the overall variance.Zn (0.83823) demonstrates the highest loading on PC4, suggesting a positive correlation between Zn concentrations and the observed variance.The loading indicates that changes in Zn levels may have a lesser but still notable impact on the overall variability of heavy metal concentrations in Cyprus conglomeratus.

Table 4 .
PCA loading of heavy metals on Salvadora persica.

Table 5 .
Pearson correlation in ionic composition of coastal flora (H.mucronatum, C. conglomeratus, S. pausiflorum and S. persica) leaf.*Significant at ≤ 0.05.**Highlysignificant at ≤ 0.01.Role of environmental factorsThe absence of significant correlations in some cases, such as in S. pausiflorum, may indicate the influence of environmental factors or seasonal variations on ion regulation.Further investigation into the impact of environmental conditions on ion composition could provide additional insights.
Vol:.(1234567890) Scientific Reports | (2024) 14:11282 | https://doi.org/10.1038/s41598-024-62201-0www.nature.com/scientificreports/ 1.69245 to 40.4949.The provided models, labeled as Model 1, Model 2, and Model 3, represent different analyses or variations of the interclass correlation (ICC) calculations for EC, pH, and ash content of Halopyrum mucronatum.Each model focuses on different aspects of the ICC calculations.ICC is a widely used statistical measure to evaluate the reliability and consistency of measurements.By applying ICC to the measurements of EC, pH, and ash content, this study can determine the extent to which the measurements are consistent within the same location (individual ICC) and across different locations (mean ICC).It provides insights into the reliability of the measurements and the degree of agreement among raters or cases.In Model 1, individual ICC (ICC (1, 1)): This value represents the ICC calculated for each specific variable (EC, pH, and ash content) within the same location.The values reported are − 0.4693 for EC, − 0.4693 for pH, and − 0.4693 for ash content.Mean ICC (ICC (1, k)): This value represents the average ICC calculated across all locations for each variable.The reported mean ICC values are − 22.93 for EC, − 22.93 for pH, and − 22.93 for ash content.In Model 2, individual ICC (ICC

Table 9 .
ANOVA and interclass correlation (ICC) analysis of Salvadora persica.and adaptation to environmental conditions.This understanding is crucial for advancing agricultural practices, conservation efforts, and ecosystem management.